This document contains results for testing row and column sampling for consensus partitioning on the five datasets ( Golub leukemia dataset, HSMM single cell RNASeq dataset, MCF10CA single cell RNASeq dataset, Ritz ALL dataset and TCGA GBM microarray dataset). For each dataset, four consensus partition methods (SD:hclust, SD:skmeans, ATC:hclust and ATC:skmeans) were applied, and each method ran for 100 times so that the variability of 1-PAC can be captured. The random sampling was done by rows or by columns. Each individual cola run was done with default parameters. The scripts for the analysis can be found here.
For each dataset, there are four plots:
Figure 1. Distribution of 1-PAC scores
Figure 2. Mean difference of 1-PAC between row-sampling and column-sampling
Figure 3. Individual partitions from row-sampling or column-sampling
Figure 4. Concordance of the partitioning by row-sampling or/and column-sampling
Figure 5. Distribution of 1-PAC scores
Figure 6. Mean difference of 1-PAC between row-sampling and column-sampling
Figure 7. Individual partitions from row-sampling or column-sampling
Figure 8. Concordance of the partitioning by row-sampling or/and column-sampling
Figure 9. Distribution of 1-PAC scores
Figure 10. Mean difference of 1-PAC between row-sampling and column-sampling
Figure 11. Individual partitions from row-sampling or column-sampling
Figure 12. Concordance of the partitioning by row-sampling or/and column-sampling
Figure 13. Distribution of 1-PAC scores
Figure 14. Mean difference of 1-PAC between row-sampling and column-sampling
Figure 15. Individual partitions from row-sampling or column-sampling
Figure 16. Concordance of the partitioning by row-sampling or/and column-sampling
Figure 17. Distribution of 1-PAC scores
Figure 18. Mean difference of 1-PAC between row-sampling and column-sampling
Figure 19. Individual partitions from row-sampling or column-sampling
Figure 20. Concordance of the partitioning by row-sampling or/and column-sampling